Supplying power to an electric vehicle

ABSTRACT

A power supply system that utilizes a hybrid architecture to enable low cycle-life, high energy density chemistries to be used in rechargeable batteries to extend the range of a traction battery.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority benefit of U.S. Application Nos.63/089,990, filed Oct. 9, 2020 and 63/161,822, filed Mar. 16, 2021, eachof which is incorporated by reference in its entirety.

TECHNICAL FIELD

The disclosure relates generally to systems, methods, and computerprograms for supplying power to an electric vehicle and, morespecifically, to systems, methods, and computer programs operating apower supply system of an electric vehicle through high energy densitybatteries configured to extend the range of a traction battery.

The disclosure also relates to a method, system, and computer programproduct for intelligent determination of the level of output power toobtain from individual batteries of a hybrid architecture to achieve arange or distance goal while maintaining or maximizing the meritsprovided by a hybrid architecture including ensuring safety, maximizingbattery life and maximizing battery capacity in electric vehicles.

BACKGROUND

Power supply systems used in electric vehicles are usually connected inseries using a single battery pack or multiple battery packs. Thesebatteries are usually rechargeable batteries and are typicallylithium-ion batteries.

Lithium-ion batteries have been widely used in electric vehicles andstorage as green energy without environmental pollution due to theirhigh output voltage, good cycle performance, low self-discharge rate,fast charge and discharge, and high charging efficiency

A traditional battery parameter update relies on a Battery ManagementSystem (BMS). The main functions of BMS include: monitoring batteryvoltage, current, temperature among other data points; estimatingbattery SOC (State of Charge), SOH (State of Health), SOE (State ofEnergy), SOP (State of Power), RM (Remaining Mileage), runningdiagnostics; protecting the battery's health and executing batterybalancing management and battery thermal management processes

To more accurately measure the battery's parameters, the conventionaltechnical solution often pre-stores an OCV (Opening Circuit Voltage)-SOCcurve for checking the estimated battery SOC. Some data may be uploadedto a cloud backup by the BMS so that a manufacturer or the after-salescan retrieve the data analysis fault and the battery historyinformation.

It is usually difficult to maintain a precise balance of the SOC andbalance the battery characteristics between the battery cells and thebattery pack/module. Old and new batteries, batteries of differentcapacities, or battery packs of different characteristics cannot be usedtogether; failure of one battery core or pack can cause the entirebattery system's failure. These problems decrease efficiency and rangeand have greatly increased the production and screening costs of batterysystems.

Another common problem that has arisen in battery technology developmentinvolves the trade-off between energy density, the number of batterycycles available during useful life, and the battery's performance. Noknown technology presently exists that provides a battery solution orenergy storage solution with favorable energy density, high performanceand a large number of cycles through which the battery can be chargedand discharged during its useful life.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced. Certain novelfeatures believed characteristic of the power supply system are outlinedin the appended claims. The power supply system itself, however, as wellas a preferred mode of use, further non-limiting objectives, andadvantages thereof, will best be understood by reference to thefollowing detailed description of the illustrative embodiments when readin conjunction with the accompanying drawings, wherein:

FIG. 1 depicts a block diagram of a power supply system in whichillustrative embodiments may be implemented.

FIG. 2 depicts a block diagram of a computer system in whichillustrative embodiments may be implemented.

FIG. 3 depicts a sketch of an electric vehicle in accordance with anillustrative embodiment.

FIG. 4A depicts a chart in accordance with an illustrative embodiment.

FIG. 4B depicts another chart in accordance with an illustrativeembodiment.

FIG. 5A depicts a sketch of a power supply system in accordance with anillustrative embodiment.

FIG. 5B depicts a chart of a chart discharge curve in accordance with anillustrative embodiment.

FIG. 6 depicts another sketch of a power supply system in accordancewith an illustrative embodiment.

FIG. 7 depicts another block diagram of a power supply system andvehicle chassis in accordance with an illustrative embodiment.

FIG. 8 depicts a block diagram of a power supply system in accordancewith an illustrative embodiment.

FIG. 9 depicts a flowchart of an example process for operating a powersupply system in which illustrative embodiments may be implemented.

FIG. 10 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented.

FIG. 11 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented.

FIG. 12 depicts a configuration for intelligent power output proposalsin accordance with an illustrative embodiment; and

FIG. 13 depicts a block diagram of an example configuration for traininga machine learning model in accordance with an illustrative embodiment.

FIG. 14 depicts a flowchart of an example process in accordance with anillustrative embodiment.

FIG. 15 depicts a block diagram of an example prioritization ofattributes in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments recognize that the presently availablesolutions do not fully address the problems discussed above or provideadequate solutions. Electric vehicles usually depend on a single batteryfor powering the vehicle. This limits the vehicles' range to onlychemistries that can meet cycle life, durability, and rangerequirements, usually meaning that the chemistries have to be limited.Many chemistries can have higher energy densities than conventionalchemistries used for electric vehicle batteries (e.g., two to threetimes higher in energy densities) but possess insufficient cycle life.Given that range extension is needed in electric vehicles, saidchemistries, when managed properly, can be utilized for significantlyextending the range outside conventional capabilities.

The illustrative embodiments recognize that most conventional cells inrechargeable batteries are connected in parallel, precluding controllinginput and output currents passing through the cells. The illustrativeembodiments also recognize that when individual cells of saidrechargeable batteries fail, it is difficult to maintain the battery'sintegrity and performance, as the death of the cell is accelerated dueto a failure to detect and/or mitigate said failure in time. Moreover,in some configurations, the entire battery may be rendered unusable whenone cell fails. The illustrative embodiments further recognize thatconventional batteries have not utilized high energy density chemistriesdue to high cycle life requirements.

For the clarity of the description, and without implying any limitationthereto, the illustrative embodiments are described using some exampleconfigurations. From this disclosure, those of ordinary skill in the artwill be able to conceive many alterations, adaptations, andmodifications of a described configuration for achieving a describedpurpose, and the same are contemplated within the scope of theillustrative embodiments.

Furthermore, simplified diagrams of systems are used in the figures andthe illustrative embodiments. In an actual computing environment,additional structures or components that are not shown or describedherein or structures or components different from those shown but for asimilar function as described herein may be present without departingthe scope of the illustrative embodiments.

Furthermore, the illustrative embodiments are described concerningspecific actual or hypothetical components only as examples. The stepsdescribed by the various illustrative embodiments can be adapted forpower supply systems for electric vehicles using a variety of componentsthat can be purposed or repurposed to provide a described operation, andsuch adaptations are contemplated within the scope of the illustrativeembodiments.

The illustrative embodiments are described concerning certain types ofsteps, applications, processors, problems, and data processingenvironments only as examples. Any specific manifestations of these andother similar artifacts are not intended to be limiting to theinvention. Any suitable manifestation of these and other similarartifacts can be selected within the illustrative embodiments' scope.

The examples in this disclosure are used only to clarify the descriptionand are not limiting to the illustrative embodiments. Any advantageslisted herein are only examples and are not intended to be limiting tothe illustrative embodiments. Specific illustrative embodiments mayrealize additional or different advantages. Furthermore, a particularillustrative embodiment may have some, all, or none of the advantageslisted above.

The illustrative embodiments described herein are directed to a powersupply system 100 for electric vehicles. The power supply system 100(FIG. 1) is configured to include low cycle life, high energy densitychemistries in a hybrid architecture to enable the benefits of suchchemistries, including significant increases in range while protectingsaid architecture from the liabilities of said chemistries that haveprevented them from otherwise being relied upon in the automotive field.Battery systems disclosed herein may be referred to as “hybrid” systemssince they include multiple chemistries working in tandem.Alternatively, to distinguish from “hybrid” vehicles that use bothelectric and internal combustion power sources, battery systems,vehicles, and related systems and components disclosed herein may bereferred to as “range-extending, multi-chemistry battery systems.”

A power supply system 100 as disclosed herein may include a tractionbattery 102 (including, for example, lithium iron phosphate (LFP)) and ahybrid range extender battery 124 comprising one or more high energydensity hybrid modules 112 that possess one or more hybrid chemistriesand that can be controlled to provide power to charge the tractionbattery 102 and/or power the electric vehicle. One or more embodimentsrecognize that an existing problem in rechargeable battery manufacturingneeds to provide electric vehicles with batteries having high energydensities that increase the range of electric vehicles availablelong-distance driving beyond conventional ranges while accounting forcorresponding low cycle life introduced by said high energy densities.

One or more embodiments includes one or more processors 106 (orprocessors 120, computer processors 206, FIG. 2) included in or outsidean on-board or external computer system 126 (or computer system 200) toperform some of the steps herein. A traction battery may monitor anddetermines the limits of the discharge and charge. An inverter maymanage a flow of power, and a hybrid module controller may manage itsown charge/discharge via a DC/DC converter. In one or more embodiments,the vehicle 302 (FIG. 3) is configured as an electric vehicle (EV). Inone or more embodiments, the vehicle 302 is configured as a plug-inhybrid electric vehicle (PHEV). The term electric vehicle is usedhereinafter to collectively vehicles such as motor vehicles, railedvehicles, watercraft, and aircraft configured to utilize rechargeableelectric batteries as their main source of energy to power their drivesystems propulsion or that possess an all-electric drivetrain.

Further, as used herein, a sensor is a sensor device that can be asystem, an apparatus, software, hardware, a set of executableinstructions, an interface, a software application, a transducer, and/orvarious combinations of the aforementioned that include one or moresensors utilized to indicate, respond to, detect and/or measure aphysical property and generate data concerning the physical property.

Further, battery energy density is used generally to refer to a measureof how much energy a cell contains in proportion to its volume.

Even further, as used herein, a high energy density module generallyrefers to a module having cells with a cell energy density of about 1000Wh/L or more, for example, with an energy density of 1100 Wh/L or 1200Wh/L. Persons of ordinary skill in the art will recognize, as shown inFIG. 4B that conventional battery chemistries with automotive levels ofperformance have cell energy densities, measured at a cell level, belowor significantly below 1000 Wh/L, for example, between about 350 Wh/Land 500 Wh/L. Using high energy density chemistries in the hybrid rangeextender battery 124 may ensure the provision of energies, for example,more than twice or three times the energies provided by the tractionbattery 102.

In one or more embodiments, the power supply system 100 comprises atraction battery 102 having one or more traction modules 122, a hybridrange extender battery 124 comprising one or more high energy densityhybrid modules 112, and a partition between the traction battery 102 andthe hybrid range extender battery 124.

Each module can be a battery stack. Those having skill in the artappreciate that other types of battery devices can be used to providepower in the embodiments described herein, and, thus, the recitation ofcertain configurations is not intended to be limiting. As discussedherein concerning FIG. 1, a battery management system, BMS 104 may use,for example, an on-board computer system 126 to control the relays 108and report operational limits. It may also request power from one ormore hybrid modules to meet a need of the vehicle. The hybrid modulecontroller of a hybrid range extender battery 124 may control itscontribution to a high voltage DC bus based on its own internal goals(such as goals defined by one or more pre-set or dynamically-determinedrules), energy state, and the observed energy state of the tractionbattery and driving behavior without centralized coordination from theBMS. Thus, the power supply system 100 can be operated in a moreefficient and power-saving mode to increase the distance of operation ofthe vehicle 302 or prevent the degradation of a module caused by asingle malfunctioning cell. For example, during a journey, one or moreembodiments described herein include an on-board computer system 126that will estimate the electrical power requirements to navigate to adestination and determine if the vehicle 302 can safely reach thedestination using the stored energy available to operate. If thecomputer system 126 determines that the vehicle cannot reach thepredetermined destination, the traction battery 102 may be charged usingthe hybrid range extender battery 124 to provide enough power for thejourney.

In one or more embodiments, the high energy density hybrid modules 112are configured to have a single chemistry, whereas in one or more otherembodiments, the high energy density hybrid modules 112 are configuredto have multiple chemistries (for example, three chemistries for daily,weekly and monthly use).

In an illustrative embodiment, the traction battery 102 comprises asingle traction module 122 or a plurality of traction modules 122connected in series. In another illustrative embodiment, the hybridrange extender battery 124 has a plurality of high energy density hybridmodules 112 connected in parallel with each other and also in parallelwith the traction battery 102, allowing each of the high energy densityhybrid modules 112 to manage their contribution to the charging of thetraction battery 102 or powering of the vehicle 302, wherein a hybridmodule controller 118 of each high energy density hybrid module 112includes a bi-directional DC-DC converter. More generally, negativeterminals are connected, and the positive output of bi-directional DC-DCconverters are connected.

In one or more embodiments, the batteries that can be utilized in thehybrid range extender battery 124 described herein to provide power tovehicle 302 or charge the traction battery 102 include batteries havingcells 114 with cell energy densities greater than 1000 Wh/L.

Battery systems in electric vehicles are typically traction batteriesand are made up of hundreds of cells that are packed together. Thesesystems, with a voltage rating of, for example, 300V to 400V, supplycurrent as high as about 300 A (e.g., 200-300 A), and any mismanagementcould trigger significant disaster. Battery management systems are thusessential in electric vehicles for the safe operation of high-voltagebatteries. They can be configured to monitor the state of the batteriesand prevent overcharging and discharging that may reduce the battery'slife span, capacity and even cause explosions. For instance, a BMSchecks the voltage, and when the required voltage is reached, it stopsthe charging process. When irregular patterns in the power flow aredetected, a BMS can shut down the battery and send out an alarm.Moreover, BMSs can be configured to relay the information about thebattery's condition to energy and power management systems. In addition,they can regulate the temperatures of the battery cells and also thebattery's health, making it safe and reliable under all conditions.

One feature of a BMS is the ability to estimate the state-of-charge(SOC) of a battery pack as it is desirable or, in some cases, criticalto efficiently maintain the SOC of the battery packs to ensure that thevoltage of the battery is not too high or too low. For example, in somecases the battery should not be charged beyond 100 percent or dischargedto 0 percent as this will reduce the capacity of the battery cells. ABMS may provide precise information on the voltage and temperature ofthe battery as well as providing an indication of the energy availablefor use and the remaining battery charge.

In some embodiments, a SOC may be estimated. Moreover, in acoulomb-counting process, the current going into or coming out of abattery is integrated to produce the relative value of its charge.However, it often may be difficult for conventional systems toaccurately determine the SOC and other characteristics of individualcells connected in parallel.

Therefore, the illustrative embodiments recognize that conventional BMSsare not capable of accurately measuring the individual characteristicsof cells in a battery pack. Conventional solutions attempt to obtainestimates but have no way of controlling a cell's current to measurecorresponding characteristic parameters, such as voltage, of the cell.

Turning back to FIG. 1, the traction battery 102 may include one or moretraction modules 122 configured to power the vehicle 302. The hybridrange extender battery 124 is designed to be modular, having one or morethan one type of chemistry, different from the chemistry of the tractionbattery 102, to provide the vehicle with its varying power requirementswhen needed. As a specific example, the traction battery may have an LFPchemistry, and the hybrid range extender battery 124 may have a Gr(Graphite) or Gr+SS (Graphite+Solid State) chemistry. Regardless of thespecific chemistry used, the hybrid range extender battery 124 may bedesigned to have one or a plurality of high energy density hybridmodules 112 or packs that are configured with respective DC-DCconverters to act as standalone batteries. By being able toindependently control the high energy density hybrid modules 112, andindependently measure the health or state of its individual cells 114, acharging and discharge rate the cells 114 can be regulated. In anembodiment, cells 114 of the high energy density hybrid modules 112 arearranged in series. By using a balance device 128 such as a bleederresistor connected in parallel with each cell 114, a rate of charging ordischarging of the cell 114 can be controlled, i.e., Turning on thebleeder resistor for a cell discharges the electric charge stored in thecell. In an illustrative embodiment, The bleeder resistor may be enabledto create an additional discharge current of up to a few hundred (200)milliamps, thereby minutely adjusting the charge/discharge current of acell and allowing the cells within the string to be brought to a commonstate. Further, one or more sensors 116 are used to measure voltages anddetermine how long the bleeder resistor should remain activated toachieve a balanced state across all cells in the series string of cells.

The rate at which a battery is discharged relative to its maximumcapacity is its C-rate. For example, a 1C rate means that the dischargecurrent will discharge the entire battery in 1 hour. Typically, avehicle needs 4C peak and 1C average. By controlling the high energydensity hybrid modules 112 individually with the bi-directional DC-DCconverters, a rate of C/5 (i.e., 0.2C) or less can be achieved. Thisprevents triggering failure events associated with high energy densitychemistries due to excessive charging and discharging. Morespecifically, a traction battery 102 may follow the load demand of thevehicle and provide the peak currents. The high energy density hybridmodules 112 may employ their bi-directional DC-DC converters todischarge into the vehicle HV bus where the traction battery andpowertrain are connected. In an illustrative embodiment that has fivehigh energy density hybrid modules 112, each contributing C/5, thentheir combined contribution is 1C. If the vehicle needs 4C, the tractionis discharged at 3C. If the vehicle needs 1C, the traction battery 102is at rest (0C). If the vehicle needs −1C (regenerative braking), thetraction battery is recharged at a 2C. In an embodiment, each highenergy density hybrid module 112 also has an operatively coupled hybridmodule controller 118 for measuring the health or state of the cells114. For example, a hybrid module controller 118 can be configured tomeasure the voltage, current, temperature, SOC (State of Charge), SOH(State of Health) for all cells of the corresponding high energy densityhybrid module 112. It also has a DC-DC converter control to allowisolation and current to be managed and throttle their contribution,both absorbing and providing energy to the main bus/high voltage DC busof the power supply system 100. The system also may have a BMS 104configured to primarily communicate with the traction battery 102. Incase a traction battery 102 malfunctions, one or more of the high energydensity hybrid module 112 can act as a replacement (e.g., temporaryreplacement) for the traction battery 102 by supplying power directly tothe drive unit 110. One or more processors (processor 120, processor106, or a processor of computer system 126) are used in a number ofconfigurations to enable the performance of one or more processes oroperations described herein. Relays 108 are controlled to operativelycouple a drive unit 110 of the vehicle to power from the power supplysystem 100. The drive unit 110 may collectively refer to devices outsidethe power supply system 100 such as a propulsion motor, inverter, HVAC(Heating, Ventilation, and Air Conditioning) system, etc.

Having described the power supply system 100, reference will now be madeto FIG. 2, which shows a block diagram of a computer system 200 that maybe employed in accordance with at least some of the illustrativeembodiments herein. Although various embodiments may be described hereinin terms of this exemplary computer system 200, after reading thisdescription, it may become apparent to a person skilled in the relevantart(s) how to implement the disclosure using other computer systemsand/or architectures.

In an example embodiment herein, the computer system 200 forms a part oris independent of computer system 126 of FIG. 1. Moreover, at least somecomponents of the power supply system 100 may form or be included in thecomputer system 200 of FIG. 2. The computer system 200 includes at leastone computer processor 206. Processor 106 and processor 120 of the powersupply system 100 may be or form part of a computer processor 206 or maybe independent of a computer processor 206. The computer processor 206may include, for example, a central processing unit (CPU), multipleprocessing units, an application-specific integrated circuit (“ASIC”), afield-programmable gate array (“FPGA”), or the like. The computerprocessor 206 may be connected to a communication infrastructure (e.g.,Network) 202 (e.g., a communications bus, a network). In an illustrativeembodiment herein, the computer processor 206 includes a CPU thatcontrols a process of operating the power supply system 100 includingcontrolling states of bi-directional DC-DC converters between highenergy density hybrid modules 112 and the traction battery 102 or driveunit 110 of the electric vehicle 302.

The display interface 208 (or other output interfaces) may forward text,video graphics, and other data about the power supply system 100 fromthe communication infrastructure (e.g., Network) 202 or from a framebuffer (not shown) for display on display unit 214 which may be adisplay of the electric vehicle 302. For example, the display interface208 may include a video card with a graphics processing unit or mayprovide an operator with an interface for controlling the power supplysystem 100.

The computer system 200 may also include an input unit 210 that may beused, along with the display unit 214 by an operator of the computersystem 200 to send information to the computer processor 206. The inputunit 210 may include a keyboard and/or touchscreen monitor. In oneexample, the display unit 214, the input unit 210, and the computerprocessor 206 may collectively form a user interface.

One or more computer-implemented steps of operating the power supplysystem 100 may be stored on a non-transitory storage device in the formof computer-readable program instructions. To execute a procedure, thecomputer processor 206 loads the appropriate instructions, as stored onthe storage device, into memory and then executes the loadedinstructions.

The computer system 200 may further comprise a main memory 204, whichmay be random-access memory (“RAM”), and also may include a secondarymemory 218. The secondary memory 218 may include, for example, a harddisk drive 220 and/or a removable-storage drive 222 (e.g., a floppy diskdrive, a magnetic tape drive, an optical disk drive, a flash memorydrive, and the like). The removable-storage drive 222 reads from and/orwrites to a removable storage unit 226 in a well-known manner. Theremovable storage unit 226 may be, for example, a floppy disk, amagnetic tape, an optical disk, a flash memory device, and the like,which may be written to and read from by the removable-storage drive222. The removable storage unit 226 may include a non-transitorycomputer-readable storage medium storing computer-executable softwareinstructions and/or data.

In further illustrative embodiments, the secondary memory 218 mayinclude other computer-readable media storing computer-executableprograms or other instructions to be loaded into the computer system200. Such devices may include a removable storage unit 228 and aninterface 224 (e.g., a program cartridge and a cartridge interface); aremovable memory chip (e.g., an erasable programmable read-only memory(“EPROM”) or a programmable read-only memory (“PROM”)) and an associatedmemory socket; and other removable storage units 228 and interfaces 224that allow software and data to be transferred from the removablestorage unit 228 to other parts of the computer system 200.

The computer system 200 may also include a communications interface 212that enables software and data to be transferred between the computersystem 200 and external devices. Such an interface may include a modem,a network interface (e.g., an Ethernet card or an IEEE 802.11 wirelessLAN interface), a communications port (e.g., a USB or FireWire® port), aPersonal Computer Memory Card International Association (“PCMCIA”)interface, Bluetooth®, and the like. Software and data transferred viathe communications interface 212 may be in the form of signals, whichmay be electronic, electromagnetic, optical, or another type of signalthat may be capable of being transmitted and/or received by thecommunications interface 212. Signals may be provided to thecommunications interface 212 via a communications path 216 (e.g., achannel). The communications path 216 carries signals and may beimplemented using wire or cable, fiber optics, a telephone line, acellular link, a radio frequency (“RF”) link, or the like. Thecommunications interface 212 may be used to transfer software or data orother information between the computer system 200 and a remote server orcloud-based storage (not shown).

One or more computer programs or computer control logic may be stored inthe main memory 204 and/or the secondary memory 218. The computerprograms may also be received via the communications interface 212. Thecomputer programs include computer-executable instructions which, whenexecuted by the computer processor 206, cause the computer system 200 toperform the methods as described hereinafter. Accordingly, the computerprograms may control the computer system 200 and other components of thepower supply system 100.

In another embodiment, the software may be stored in a non-transitorycomputer-readable storage medium and loaded into the main memory 204and/or the secondary memory 218 using the removable-storage drive 222,hard disk drive 220, and/or the communications interface 212. Controllogic (software), when executed by the computer processor 206, causesthe computer system 200, and more generally the power supply system 100,to perform some or all of the methods described herein.

Lastly, in another example, embodiment hardware components such asASICs, FPGAs, and the like, may be used to carry out the functionalitydescribed herein. Implementation of such a hardware arrangement so as toperform the functions described herein will be apparent to personsskilled in the relevant art(s) in view of this description.

FIG. 4A shows a chart according to an illustrative embodiment. The chartshows a percentage of driving days axis 402 and a daily driving distanceaxis 404 for an example embodiment as disclosed herein. By measuring thedriving habits of a user, it can be seen that a significant percentageof days spent driving are spent driving relatively short distances andthus utilizing the traction battery 102 as shown by a traction batteryportion 406 of the chart. On the other hand, the hybrid range extenderportion 408 is used for a comparatively much shorter amount of time. Ithappens then that, as shown in FIG. 4B, a range extender with achemistry that provides an energy density of 1000 Wh/L or more mayprovide a good trade-off of density vs. cycle life. By determining thepercentage of cycles that fall outside daily use and selecting theappropriate chemistry that can sustain that many cycles, theillustrative embodiment of FIG. 4A can be achieved.

The chart of FIG. 4B includes an energy density axis 410 and a cyclelife axis 412. As used herein, the “cycle life” of a battery refers tothe number of times the battery may be depleted to 100% depth ofdischarge (DoD) while still holding at least 80% of its original charge.So, for example, a battery having a cycle life of 100 cycles would hold80% of its original charge after being charged and completely depleted100 times.

A traction battery chemistry may be selected from a traction batterychemistry area 414 to provide a cycle life of about 3000 cycles (forexample, at least 2500 or 3000 cycles). In conventional batterychemistries, this cycle life typically provides a corresponding cellenergy density of about 400 Wh/L. To accommodate a predetermined rangerequirement for non-traction applications, a range battery chemistry maybe selected from an illustrative hybrid range extender battery chemistryarea 418 (for example, between 1000 and 1200 Wh/L). This typicallyprovides a corresponding cycle life of about 200 cycles (for example,between 200 and 350 cycles) or less. Depending on the energyrequirements of a vehicle, other chemistries 416 can be optionallyutilized for medium-range requirements and corresponding packscontrolled independently.

More generally, embodiments disclosed herein may use multiple batterychemistries in a power supply system, each of which may have differentexpected cycle lives and/or cell energy densities. This may allow use ofbattery chemistries and arrangements that conventionally are consideredunsuitable for electric vehicles and similar devices. For example,conventional systems have often presumed that a higher cycle life isnecessary, even at the expense of higher energy density. In contrast,embodiments disclosed herein can make use of higher-density chemistrieseven where the associated battery may have a relatively low cycle life,since the range extender or intermediate-range battery cells may not beput through charge/discharge cycles as often as the regular-use tractionbattery.

As a specific example, a hybrid power supply system as disclosed hereinmay include a traction battery having a cell energy density of not morethan about 500 Wh/L, 450 Wh/L, 400 Wh/L, 350 Wh/L, 300 Wh/L, or less,more generally in the range of 300-500 Wh/L, but a relatively high cyclelife of 2000 cycles, 2500 cycles, 3000 cycles, or more, more generallyin the range of 2000-3200 cycles.

A higher-density battery cell used for a range extender battery or anintermediate battery as disclosed herein may have a relatively highercell energy density of 800 Wh/L, 1000 Wh/L, 1100 Wh/L, 1200 Wh/L, ormore, or in the range 800-1400 Wh/L, and a relatively lower expectedcycle life of 300, 400, or 500 cycles or less, or in the range of100-500 cycles or less. Other battery types and chemistries may be used,especially in embodiments that use more than two chemistries. Forexample, any of the battery types shown between the traction area 414and the range extender area 418 in FIG. 4B may be used for anintermediate density battery, which may have a cycle life in the rangeof 1000-2000 cycles and an energy density in the range 500-800 Wh/L.

A figure of interest for battery chemistries used with embodimentsdisclosed herein is the energy density per cycle (EDC), determined asthe ratio of the cell energy density of the battery to the expectedcycle life. For example, as shown in FIG. 4B, an HE traction battery mayhave a cell energy density of about 400 Wh/L and a cycle life of 3000cycles, resulting in an EDC of about 0.13 Wh/L/cycle. In contrast, asolid state battery in the range extender area 418 of FIG. 4B may havean energy density of about 1000 Wh/L and a cycle life of about 400cycles, resulting in an EDC of about 2.5 Wh/L/cycle. Conventionalbattery chemistries having an EDC of 1.0 or more have previously beenconsidered unsuitable for use in electric vehicles due to the relativelylow cycle life. As previously disclosed, embodiments provided hereinallow for such batteries to be used efficiently in electric vehicleswhen used in tandem with other chemistries.

As a specific example, embodiments disclosed herein may use a tractionbattery having an EDC of about 0.12-0.16 Wh/L/cycle and a range extenderbattery having an EDC of 1.0 or more, 2.0 or more, 5.0 or more, or anyintervening value. Other chemistries may be used as well; for example,where three chemistries are used, the traction battery may have an EDCof 0.12-0.16 Wh/L/cycle and other batteries in the system may have anEDC between that of the traction battery and a highest-density battery,with the highest-density battery having an EDC of 1 Wh/L/cycle or more.

More generally, any number of battery chemistries may be used in tandem,with a “daily” traction battery having a lower EDC and more special-usebattery chemistries having higher EDC values. As another example, asingle battery chemistry in the daily use traction area 414 may be usedin conjunction with any number of batteries in the range extender region418, and/or any number of batteries in any intermediate range shown inFIG. 4B. For example, a third battery chemistry may be used inconjunction with the traction and range extender batteries previouslydisclosed, with the third chemistry having a cell energy density from400 to 1200, 1300, or 1400 Wh/L or more.

FIG. 5A shows an illustrative embodiment of a power supply system 100.The system includes a traction battery 102, a plurality of high energydensity hybrid modules 112 connected in parallel to a main tractionbus/high voltage DC bus, a plurality of traction modules 122, aplurality of bi-directional DC-DC converter 502. In addition, it has anon-board AC-DC charger 504 for recharging the power system from thegrid, a 12 V battery 512 for powering lights and ignition of thevehicle, an auxiliary DC-DC converter 506 for maintaining the 12 Vbattery 512 and providing power to the 12V systems of the vehicle. Theembodiment also has contactors 508 for switching various circuits on oroff and a control module 510 for controlling the power supply. Byplacing the 12 V battery 512 within the power supply system (inside thetraction battery 102) instead of outside as is done in conventionalsystems, the contactors 508 can be controlled, for example, kept closed,even if there are other momentary issues with the 12V system. In anillustrative embodiment, a momentary (for example, about 100 ms or more)loss of battery power could cause the contactors to open. This losscould be caused by a single bad wire external to the battery pack. Bybringing the 12V inside the pack, this risk may be reduced.

In an embodiment such as shown in FIG. 5A, each high energy densityhybrid module 112 has about 56 cells 114 connected in series. Thespecific number of cells is illustrative, and other numbers of cells maybe used without departing from the scope of the present disclosure. Anoperatively coupled hybrid module controller 118, such as an on-boardhybrid module controller 118, is configured to measure the voltage,current, temperature, SOC, and SOH of each of the individual cells 114.Each of the 56 cells 114 may have an associated voltage sensor 116.Knowing the current passing through the cell 114 and temperature (suchas the temperature of various points on the high energy density hybridmodule 112), the SOH, SOC, and other parameters for the cells 114 can becalculated to determine whether the energy output of the correspondinghigh energy density hybrid module 112 can be connected to the tractionbattery 102 or in some cases the drive unit 110 through a correspondingbi-directional DC-DC converter 502. Moreover, a bi-directional DC-DCconverter 502 for each high energy density hybrid module 112 can be usedto precisely control the current input and output for each high energydensity hybrid module 112, unlike in load following conventional powersupplies, which have no control over changing drive power. In anillustrative embodiment, charge and discharge pulses are generated forthe high energy density hybrid modules 112. By controlling the amount ofcurrent for the series-connected cells 114 of the high energy densityhybrid module 112 through the use of a bi-directional DC-DC converter502 and measuring the voltages of each of the cells 114, the impedancesof each of the cells 114 can be computed and compared to reference data,to identify any unwanted deviations in a cell impedance and acorresponding change in the health of the cell.

Current input for each high energy density hybrid module 112 may comefrom the charger after the traction battery is charged or mostlycharged. For maintenance and/or diagnostic purposes, a hybrid modulemight be discharged and recharged when not strictly needed as a rangeextender. For example, if it has been several months since a hybridmodule has been used as a range extender, it might be discharged andrecharged during normal daily use to exercise the cells. How often therange extender battery is discharged and recharged outside of normaluse, or even whether to perform such discharging/charging may beselected based upon the particular chemistry or chemistries used in therange extender battery.

The hybrid module controller 118 also may manage the strain of the cells114 by monitoring and bringing them into alignment. For example, whenone cell 114 (Cell A) is determined to be at a lower SOC (e.g., 20%)than another cell 114 (Cell B) that is connected in series (70%), Cell Bwill reach a full charge earlier than Cell A, thus requiring thecharging of Cell B to be halted to prevent overcharging it. By reducingthe SOC of Cell B to that of Cell A using a bleeder resistor, Cell A andCell B can both be charged at the same rate to a predetermined fullcharge. Thus, the hybrid module controller 118 keeps the SOC of the 56cells 114 equal or substantially equal (e.g., within +/−10%, or +/−5%,or +/−1%), such that a full range of the module can be used. In anotherexample, by determining cells 114 with lower self-discharge rates thanthat of other cells 114, the hybrid module controller 118 determineswhich cells 114 to selectively discharge to a determined charge in orderto subsequently charge all 56 cells 114.

In another illustrative embodiment, because the high energy densityhybrid modules 112 are connected in parallel to each other andindependently controlled, an individual high energy density hybridmodule 112 may be separately removable for reconditioning by slowingcharging and discharging it without affecting the normal operation ofthe power supply system 100.

FIG. 5B illustrates an example charge-discharge curve 500 of a cellwhich includes a voltage axis 514, a capacity axis 516, discharge curves518, and charge curves 520. As illustrated in the discharge curves 518,at high discharge currents/C-rate 522 rate (e.g., 5C), the cell capacityis not fully utilized, and the cell voltage drops due to internalresistance. Current flowing through a cell causes an IR voltage dropacross an internal resistance of the cell, which decreases the terminalvoltage of the cell during discharge and increases the voltage needed tocharge the cell, thus reducing its effective capacity as well asdecreasing its charge/discharge efficiency. Higher discharge rates giverise to higher internal voltage drops, which explains the lower voltagedischarge curves at high C-rates 522 and characteristically differentshapes of the curves. By discharging and charging at the various C-rates522, due to an ability to control currents precisely using thebi-directional DC-DC converters 502, any cell's impedance problem can bededuced and mitigated by comparing it to a reference profile such as apreviously stored profile the cell 114. This can be achieved using acontrolled step response to characterize the behavior of cell 114 withtime. One mitigation operation includes discharging high energy densityhybrid modules 112 having no identified cell issues first. Anothermitigation operation includes slowing down a discharge of a high energydensity hybrid module 112 with an identified cell impedance issue.

FIG. 6 shows another example configuration of a power supply system 100as disclosed herein, which includes an on-board energy management system602. In this example the traction battery 102 has a capacity of 44 kWhand provides a voltage of 320V, and a hybrid range extender battery 124has a capacity of 120 kWh through six 20 kWh high energy density hybridmodules 112, each having a voltage of 48V. The on-board energymanagement system 602 has a battery management system (not shown) and isconfigured as a tri-voltage system to handle the 12V, 48V, and 320V.Moreover, the on-board energy management system 602 provides sixbi-directional DC-DC converters (not shown), with each one operativelycoupled to a high energy density hybrid module 112. By configuring thebi-directional DC-DC converters to provide a power of, for example, 10kW, the energy management system 602 can provide 60 kW (6×10 kW)bi-directional 48-500 V DC-DC with a 98.5% peak efficiency. Of course,the particular arrangement of voltage, power capacity, and otherfeatures is non-limiting, and other configurations can be obtained inlight of this specification. The examples in this disclosure are usedonly for the clarity of the description and are not limiting to theillustrative embodiments. Additional operations, actions, tasks,activities, and manipulations will be conceivable from this disclosure,and the same are contemplated within the scope of the illustrativeembodiments.

Conventional battery capacities of current electric vehicles range froma mere 17.6 kWh in some smart cars with a range of just 58 miles, up to100 kWh in some Tesla models (Tesla is a trademark of Tesla, Inc. in theUnited States and in other countries). By introducing a scalablearchitecture, as shown in FIG. 7, various configurations can be providedto meet different range requirements. In the illustrative embodiment ofFIG. 7, by providing five additional high energy density hybrid modules112 in configuration 2 704 than in configuration 1 702, availablecapacity is increased from 130 kWh to 200 kWh, and by introducinganother five additional high energy density hybrid modules 112, toconfiguration 2 704, a 270 kWh capacity is obtained for configuration 3706. Moreover, the scalable architecture allows an unrestrictedplacement of individual modules at different locations in a vehicle,outside a conventional placement on a chassis 304 (FIG. 3) of a vehicle302, since each module only needs to be individually connected to thetraction battery or a high voltage DC bus. As disclosed herein, forexample with respect to FIG. 4, various high energy density modules mayuse different chemistries, allowing for additional flexibility in usecases, energy density and expected cycle life, and the like.

FIG. 8 shows another configuration of the power supply system having atraction battery 102, a plurality of high energy density hybrid modules112, and a plurality of bi-directional DC-DC converters 502. In theconfiguration, a module is disabled due to a SOH check indicating anissue with a cell 114. The disabled hybrid module 802 is taken offlineand may undergo a formation recharge to extend its life wherein themodule is slowly discharged over, for example, a 20 hour period andslowly recharged over, for example, another 20 hour period, at a definedtemperature, in order to rebuild its chemistry. The modular nature ofthe configuration provides that the vehicle is still usable during theformation recharge without a need to physically remove the disabledhybrid module 802. In an example herein, bleeder resistors of the cells114 are used in the charging and discharging operations.

The figure also shows a reduced capacity hybrid module A 804, a reducedcapacity hybrid module B 806, and a regular capacity hybrid module 808.The hybrid module controller 118 of reduced capacity hybrid module A 804or of reduced capacity hybrid module B 806 is configured to detect anissue with a cell 114 and independently make a decision on its dischargerate, for example, by reducing a power output from 2 kW to 1 Kw.

In step 902, process 900 provides for a traction battery comprising oneor more traction modules controlled by a Battery Management System (BMS)to be connected to and disconnected from a high-voltage DC bus of anelectric vehicle 302. Herein, the traction battery is configured topower the electric vehicle 302. In step 904, process 900 provides ahybrid range extender battery 124 comprising a plurality of high energydensity hybrid modules 112 connected in parallel with each other and tothe high voltage DC bus to which the traction battery 102 is alsoconnected. Each high energy density hybrid module 112 of the pluralityof high energy density hybrid modules 112 includes a correspondinghybrid module controller (HMC) and a plurality of cells 114 connected inseries. The health of each cell 114 of the plurality of cells 114 isconfigured to be independently measurable by the corresponding HMC. ASOC of each cell can also be controlled through a balance device 128,such as a bleeder resistor connected in parallel with cell 114. Thecells of each module can thus be controlled independently and as awhole.

In step 906, a plurality of bi-directional DC-DC converters 502 arearranged between the plurality of high energy density hybrid modules 112and the high-voltage DC bus of the electric vehicle 302, and/or betweenthe plurality of high energy density hybrid modules 112 and the tractionbattery 102.

Process 900 operatively couples a Direct Current from one or more of theplurality of high energy density hybrid modules 112 to the tractionbattery 102 (step 908) and/or to the high-voltage DC bus of the electricvehicle 302 (step 910) to charge the traction battery and/or power theelectric vehicle 302 respectively. In step 912, process 900 controls apower generating mode of the power supply system by obtaining sensorinformation about the independently measurable cells 114. In step 914,process 900 controls, using each HMC of the plurality of thecorresponding HMCs, a rate of charging and discharging of itscorresponding high energy density hybrid module through sensorinformation obtained about the independently controllable cells.

Intelligent Power Control

The illustrative embodiments further recognize that traditional electricvehicle power systems that are configured to estimate a state of health(SOH) or state of charge (SOC) of component batteries are mostlyreactive, incapable of predicting energy consumption needs andrestricted to making use of remaining available energy in a largelyretrospective manner. The illustrative embodiments recognize that whileestimates can be presently obtained according to the perceived states ofinterest, little to no mitigation measures are available to ensurebatteries' safety or preserve their available life and capacities.Moreover, the load following nature of conventional electric vehiclepower computer systems, which have no control over changing drive power,means that the current input and output for battery modules cannot beprecisely controlled.

As far as managing the chemistries of individual modules of a powersupply system, existing conventional batteries charge and discharge allindividual modules together. However, embodiments disclosed hereinrecognize that monitoring the chemistries of individual battery modulesin a larger power supply system and controlling them individually toensure the safety of the system as a whole may provide additionalbenefits not available in conventional battery systems and electricvehicles. For example, by being unable to disable individual modules fora formation recharge without the need to disable the larger power supplysystem in a conventional system, the safety of the power supply systemcannot be guaranteed, and the available life cycles of individualmodules are unduly shortened from overcharging and over-discharging.

Embodiments disclosed herein recognize that presently available tools orsolutions do not address the need to provide intelligent management ofindividual modules in a hybrid architecture to provide additional powerwhen needed while preserving or maximizing battery life cycles and thusthe lifetime, safety, and maximum capacity of the individual modules ina manner that allows the achievement of range and distance goals. Theillustrative embodiments used to describe the invention may address andsolve the above-described problems and other related problems by theintelligent supply of power to an electric vehicle through high energydensity hybrid modules 112 in a power supply system. The illustrativeembodiments may solve these problems in a proactive and/or preparatoryprocess that anticipates the power demands of electric vehicles andoperates to meet said demands.

Certain operations are described as occurring at a certain component orlocation in an embodiment. Such locality of operations is not intendedto be limiting on the illustrative embodiments. Any operation describedherein as occurring at or performed by a particular component, e.g., apredictive analysis of battery data and/or a natural language processing(NLP) analysis of contextual calendar data, can be implemented in such amanner that one component-specific function causes an operation to occuror be performed at another component, e.g., at a local or remote machinelearning (ML) or NLP engine respectively.

An embodiment monitors and manages the cumulative energy of hybrid powersupply systems. Another embodiment monitors a variety of profile sourcesconfigured for the user. A profile source is an electronic data sourcefrom which information used to determine a user's profile characteristiccan be obtained. For example, a profile source may be user's preferencesconfiguration on a computing device such as a required speed or route, acalendar application where the user's future events are planned, andpast events are recorded, a destination in a global positioning system(GPS) application where the user enters a current destination, feedbackfrom a user or community and the like. A profile source can be a device,apparatus, software, or a platform that provides information from whicha driving characteristic of the user can be derived. For example, anelectric vehicle dashboard can operate as a profile source within theillustrative embodiments' scope. Moreover, a community such as a fleetof electric vehicles can be a profile source wherein a plurality ofdriving characteristics of user-profiles may be obtained to derive apreference, liking, sentiment, or usage of electric vehicles. Further,measured health metrics or parameters about individual modules ofbattery packs of the fleet of vehicles may be a profile source from thecommunity and may be utilized to learn from and derive patterns fordelivering power in a subject electric vehicle. Thus, batteries from afleet of vehicles may adapt their predictions and share values forprediction/proposal purposes.

A user's profile data, information, and preference are terms that areused herein interchangeably to indicate a constraint of one or moreusers that affects the delivery of power in the power supply system.Furthermore, information/data about the electric vehicle and powersupply system 100 (such as vehicle speed, module current, temperature,voltage, impedance, state of health, state of charge, average energyconsumption, and the like or otherwise subject electric vehicleparameters 1220) may form part of or be separate from the constraintsand may be obtained for use as input to an intelligent power controlmodule for predictive analytics as described hereinafter. Thus, theprofile source information and the electric vehicle and power supplysystem data (subject electric vehicle parameters 1220) collectively format least a part of the input data 1202 or constraints for theintelligent power control module to predict the level of output power toobtain from individual batteries of a hybrid architecture in order toachieve a range or distance goal while accounting for safety, batterylife and battery capacity of power supply systems 100 in electricvehicles, hereinafter referred to as attributes of the power supplysystems 100.

Therefore, input data can be determined directly from measurementsobtained from components of the electric vehicle. The input data canalso be directly indicated in the information of a profile source. Forexample, a user may have an expressly stated preference for destinationarrival time or a range goal during a specified period or until furthermodification of the preference.

The input data can also be derived from the information collected from aprofile source. For example, an embodiment can be configured to analyzea calendar of a user to derive a destination arrival time. Furthermore,information such as texts or comments in a driving network may beanalyzed, for example, contextually to determine upcoming traffic. Inanother example, the landscape of a geographic area may be obtained froman environment profile and examined to establish the nature of theterrain (e.g., the presence of steep slopes in a mountainous region asobtained from an imaging apparatus or database) and thus the need toincrease the power output of a battery.

The input data as determined by an embodiment may be variable over time.For example, the user may prefer a predetermined route during shortdriving distances to and from work and may prefer a route that optimizesenergy consumption during long-distance vacation trips. Thus, thepreference can change when a vacation driving characteristic obtainedfrom a user profile becomes a priority. In that case, the intelligentpower control module may prioritize the use of a high energy densityhybrid module 112 of a hybrid range extender battery 124 over the use ofa traction module 122 of a traction battery 102 in order to extend therange of the traction battery 102.

Similarly, a drive to work may not require the use of a hybrid rangeextender battery 124. However, due to a determination that a normal workroute has traffic and a contextual establishment that, for example, auser has a meeting in 1 hour, an “expeditious driving characteristic” ofthe user may be prioritized, thus causing the vehicle navigation systemto abandon a normal route in favor of a new, albeit mountainous route.Based on predictive analytics about power or energy needed to traversethe mountainous route in under 1 hour being greater than an availabletraction battery power or energy or greater than at least a thresholdpower or energy, the power control module determines that a high energydensity hybrid module 112 is needed to complete the drive to work. Evenfurther, the power control module may be configured to independently andautomatically pre-charge the traction battery 102 via bi-directionalDC-DC converters 502 connected to the high energy density hybrid modules112 to at least a threshold charge in anticipation of the drive upon thecontextual establishment of the meeting.

Importantly, the power control module may control output power obtainedfrom the one or more high energy density hybrid modules 112 whileconcurrently ensuring that the safety, maximum life cycle, and maximumcapacity attributes of the individual high energy density hybrid modules112 are considered. For example, upon determining, based on sensorinformation obtained about the independently measurable cells of a highenergy density hybrid module 112 A, that the high energy density hybridmodule 112 A has a fault, the power control module may deactivate moduleA and utilize high energy density hybrid module 112 B to pre-charge thetraction battery 102, thus ensuring the safety of the battery pack andallowing the eventual restoration of deactivated module A through aformation recharge. In another example, upon determining that highenergy density hybrid module 112 C has six life cycles remaining, thepower control module may prioritize depleting module C before utilizingpower from another module. User feedback indicative of the accuracy ofoutput power to be retrieved from a high energy density hybrid module112, determined by the power control module, is used to modify the powercontrol module to produce better results.

Operating with profile information from one or more profile sources, anembodiment routinely evaluates the constraints that are applicable tothe user of the electric vehicle. The embodiment may add newconstraints/input data when found in profile information analysis,modify existing constraints when justified by the profile informationanalysis, and diminish the use of past constraints depending on thefeedback, the observed usage of the constraint, and/or presence ofsupport for the past constraint in the profile information. A pastconstraint can be diminished or aged by deprioritizing the constraint bysome degree, including removal/deletion/or rendering ineffective thepast constraint.

Sources of profile information may include, for example, calendarentries in a phone, tablet, or other device paired to the vehicle and/orthe management system; scheduling accounts, apps, or the like associatedwith a user to which the user may provide access; direct entry ofinformation by a user; or the like. More generally, profile informationmay be obtained from any source available to the vehicle directly orindirectly which can be associated with a user or owner of the vehicle.

Operating with profile information from one or more profile sources, anembodiment forecasts the user's activity during future periods of time.For example, based on calendar data, the embodiment may determine, forexample by NLP of the calendar entries, that the user plans to work atlocation A tomorrow at 9 AM, get lunch from 12 PM till 1 PM, and visit adoctor out of state after 3 PM. The embodiment derives energyrequirements for tomorrow based on the calendar activities and eitherpre-charges the traction battery 102 using one or more high energydensity hybrid modules 112 or assigns a high energy density hybridmodule 112 to be used tomorrow. The assignment can also be done withoutthe use of NLP to interpret the calendar data, as it is not intended tobe limiting. Further, these examples of input data/constraints,prioritization, secondary considerations, etc., are not intended to belimiting. From this disclosure, those of ordinary skill in the art willbe able to conceive many other aspects applicable towards a similarpurpose, and the same are contemplated within the scope of theillustrative embodiments.

The intelligent power control systems and techniques described hereingenerally are unavailable in the conventional methods in thetechnological field of endeavor pertaining to electric vehicles. Amethod of an embodiment described herein, when implemented to execute ona device or data processing system, comprises substantial advancement ofthe functionality of that device or data processing system in poweroutput proposals by obtaining constraints proposals and using a hybridbattery architecture that enables control of input and output currentswhile ensuring maximization of the safety, life and capacity attributesof modules of the hybrid battery architecture.

In further embodiments, a machine learning engine may be provided toincrease the resolution and efficacy of predictions made by a controllerbased on a comparison of sensed and received information. The machinelearning engine may detect patterns and weigh the probable outcomes andenergy demand profiles based on these patterns. As a user engages with avehicle, data regarding a trip may be collected and stored for analysisby the controller or another network-connected computerized device. Dataregarding trips by multiple users in multiple electric vehicles may beaggregated to allow additional resolution in detecting patterns andpredicting behavior.

For example, a driver may be traveling down a road, such as a countyroad that connects with an interstate. The geolocation sensors maydetect that the vehicle is on the road and heading in the direction ofthe interstate. Data gathered by multiple vehicles may indicate that amajority of vehicles traveling down this county road in the direction ofthe interstate are likely to enter the interstate. The data gathered bymultiple vehicles may initially indicate that drivers typically enterthe interstate in a southbound direction, for example, the direction ofa city or location with multiple workplaces.

The machine learning engine may use this information to predict anenergy demand profile for a trip. This profile may be provided as abaseline, as it may be altered as real-world conditions may deviate fromthe predicted trip. With consideration to the example above, the driverof an electric vehicle may deviate from the predicted trip and drivepast the interstate onramp. The machine learning engine may thendetermine a deviation from the predicted trip that has occurred andupdate the energy demand profile to reflect the next most likelyscenario, such as traveling to a commonly-visited relative's houselocated 30 miles beyond the access point to the interstate, or anotherlocation.

In another example, provided without limitation, sensors located on avehicle may detect the amount of torque required to move the electricvehicle in a forward direction. In this example, it may be determinedthat a substantially larger amount of torque is required to acceleratethe vehicle. It may also be determined that a larger amount ofregenerative energy is produced as the vehicle slows. The machinelearning engine may determine that the vehicle is towing another mass.Calculations may be performed by the controller to modify the amount ofstored energy required to complete a trip while towing the mass. Thesemodifications may be applied and may affect the energy demand profile toreflect the trip's additional energy demands. For example, thecontroller may adjust the energy demand profile to anticipate a higherenergy demand when towing a mass.

The machine learning engine's predictive profile may include anaggregated or baseline profile, which may indicate the general routecharacteristics and energy consumption needs from users as a whole. Themachine learning engine's predictive profile may also include a localprofile indicative of common trips performed by a user, commondestinations, and other common characteristics relating to the same. Inone embodiment, an energy demand profile may be generated per user. Andthis embodiment, a user may be identified by a dedicated key fob, amobile computing device connected to the entertainment system of avehicle, voice recognition, seat weight sensor, and/or other informationindicative of the identity of an operator. The machine learning engine,upon recognizing the operator, may adjust its predictive model to adaptto the statistically probable routes, driving habits, and other usefulcharacteristics of the operator. The energy demand profile may beadjusted accordingly with regard to the local profile associated withthe operator.

The machine learning engine may operate by updating vehicle parameterassumptions and predicting a destination-weighted energy demand. At theonset of a trip, assumptions regarding the vehicle and an anticipatedtrip may be made. These assumptions may be supported by informationdetermined by the vehicle and may be recorded as time-series data thatmay be used to calculate physics parameters. Example physics parametersmay include speed, battery system net power, traction motor power,geolocation, and other parameters as would be appreciated by a person ofskill in the art after having the benefit of this disclosure. Additionalinformation may be derived, such as relating to the geolocationinformation, such as latitude, longitude, heading, altitude, velocity,acceleration, inertia, and other information.

The vehicle-derived information may be supplemented by informationsourced via the network. Such information may include wind speed,weather information, route, distance to destination, elevation andterrain profile of a route, traffic, and other information that mayaffect the energy demands of a trip.

The machine learning engine may perform an analysis on the time seriesdata gathered at the vehicle, supplemental information such as thatprovided over a network, and/or other information to draw correlations.For example, the machine learning engine may perform a linear algebraregression analysis on the time series step data to find the best-fitvehicle parameter values. Examples of best-fit vehicle parameter valuesmay include mass, rolling resistance coefficient, aerodynamic dragcoefficient, and other values that those of skill in the art wouldappreciate. The machine learning engine may additionally return vehicleparameters, for example, that may be used by the controller in energymanagement, such as mass, a rolling resistance coefficient, aerodynamicdrag coefficient, average auxiliary electrical power load, and otherreturn parameters that those of skill in the art would appreciate. Anexample calculation that may be used to determine average auxiliaryelectrical load power may be the sum of the battery system net powerminus the sum of the traction motor power, without limitation.

The machine learning engine may advantageously assist with predicting adestination weighted energy need. This energy need may assist withdetermining whether to migrate electrical energy stored by thehigh-energy range battery to the high-power traction battery for use byan electric vehicle or other loads. In making the prediction, themachine learning engine may determine a route from a present location tovarious candidate charging locations. Trip information may be receivedby a navigation system included by a vehicle, directions provided by auser's mobile computing device, predicted based on the history of driverbehavior, or the like. The presence of a charging location, such as auser's home or public charging facilities, may be determined based ontrip history, internet provided sources, navigational directions, andother sources.

Charging location candidates may be favored if located within anacceptable proximate range of a trip destination. Favorite charginglocations may be promoted in the calculation of anticipated energydemands by the machine learning engine. Similarly, disfavored charginglocations may be deemphasized and/or removed when determining theanticipated energy demands of a trip.

Continuing the example given above, the machine learning engine maycalculate various route options to direct an operator from an originlocation to an indicated destination. The trip options may considerfactors such as the presence of charging facilities, anticipated roadstops, acceptable distances between charging facilities, unacceptabledistances between charging facilities, elevation changes, traffic, andother characteristics relating to a respective route option. The machinelearning engine may disfavor route options in which it appears areinaccessible with a current state of charge of an operator's vehicle.

In another example, provided without limitation, the machine learningengine may determine that the state of charge of the high-power tractionbattery is at about 50%. In this example, a first route, such as a mostdirect route, may need at least a 75% state of charge to reach acharging facility under normal operation. An alternative route may beidentified that presents the user with the charging facility thatrequires only 25% state of charge to reach. The machine learning enginemay then recommend the route providing earlier access to the chargingfacility and thus avoiding the necessity of bringing online at leastpart of the high-energy range battery.

Also, in this example, the operator may choose to override therecommended route, such as by driving on an alternative route. If it isdetermined that the operator chooses to take the disfavored route andbegins heading in a direction indicative of following the disfavoredroute, the machine learning engine may direct that at least part of thehigh-energy range battery be brought online to provide supplementalenergy that may be required to reach the charging facility locatedoutside of an expected capacity remaining in the high-power tractionbattery.

The machine learning engine may provide various weights to sensedinformation, conditions, parameters, trip details, and other factorsthat may influence an estimated energy consumption required to complywith a predicted energy use profile, as will be appreciated by those ofskill in the art. Example parameters that may be weighted to affect thepredicted energy use profile may include geolocation, GPS location, timeof day, day of the week, the mass of the vehicle, mass being towed by avehicle, temperature, auxiliary power demands, rolling resistancecoefficient, aerodynamic coefficient area, time since the batterypackage was last charged, time since the last charging session occurredat the candidate location, and/or other factors and parameters thatwould be apparent to a person of skill in the art after having thebenefit of this disclosure.

The machine learning engine may then correlate these parameters topredict an energy need at least partially based on the weightedinfluence of considered parameters. For example, the machine learningengine may apply a calculation that considers the energy needs to beapproximately equal to the sum of the mass included by the vehicle andother mass being towed by or carried by the vehicle. This value may bemultiplied by the anticipated energy required to complete an anticipatedroute. The machine learning engine may then analyze these factors andpredict an anticipated energy need profile relating to the anticipatedtrip. The controller of the battery package may then move power betweenthe high-energy range battery and the high-power traction battery tocompensate for any predicted deficiencies in the state of chargecurrently held by the high-power traction battery.

The redundancy features will now be discussed in greater detail. In oneembodiment, redundancy features may be provided to mitigate the risk ofone or more battery components experiencing total depletion of storedenergy and/or failure. Multiple energy management components may beincluded, so that failure of one energy management component is unlikelyto lead to failure of the system as a whole. In one example, a batterypackage may be included as a modular component, including a connectedelectric battery management component, high-power traction batterymodules, high-energy range battery modules, cooling features, and/orother aspects to assist with storage and power delivery. In thisexample, should one of the modular components fail, the remainingmodular components may continue to provide power delivery from itsconnected aspects.

In one embodiment, an independent observer module may be included toprovide backup functionality otherwise provided by the energy managementcomponent. In this example, the independent observer module may continueto run a vehicle or other connected load from the electrical energystored in the battery package, even in the case of failure of the energymanagement component otherwise connected to a respective batterypackage. For example, in a case of failure of connected energymanagement components, the redundant features of the independentobserver module may take over the operation of energy management so thatthe connected load, for example, vehicle, may continue to operatesubstantially safely until such problems causing intervention by theindependent observer module may be investigated and/or repaired. Byproviding such redundancy and safety features to mitigate failures ofthe system should they occur, a system enabled by this disclosure may becertified as an ASIL D architecture.

The illustrative embodiments are described with respect to certain typesof data, functions, algorithms, equations, model configurations,locations of embodiments, additional data, devices, data processingsystems, environments, components, and applications only as examples.Any specific manifestations of these and other similar artifacts are notintended to be limiting to the invention. Any suitable manifestation ofthese and other similar artifacts can be selected within the scope ofthe illustrative embodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the invention, either locally at a dataprocessing system or over a data network, within the invention's scope.Where an embodiment is described using a mobile device, any type of datastorage device suitable for use with the mobile device may provide thedata to such embodiment, either locally at the mobile device or over adata network, within the illustrative embodiments' scope.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the description's clarity. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures, therefore, may be used inconjunction with such embodiment of the invention within the scope ofthe invention. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure, and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

With reference to the figures and in particular with reference to FIG.10 and FIG. 11, these figures are example diagrams of data processingenvironments in which illustrative embodiments may be implemented. FIG.10 and FIG. 11 are only examples and are not intended to assert or implyany limitation with regard to the environments in which differentembodiments may be implemented. A particular implementation may makemany modifications to the depicted environments based on the followingdescription.

FIG. 10 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented. Data processingenvironment 1000 is a network of computers in which the illustrativeembodiments may be implemented. Data processing environment 1000includes network/communication infrastructure 1002.Network/communication infrastructure 1002 is the medium used to providecommunications links between various devices, databases, and computersconnected within a data processing environment 1000.Network/communication infrastructure 1002 may include connections, suchas wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network/communication infrastructure 1002 and arenot intended to exclude other configurations or roles for these dataprocessing systems. Server 1004 and server 1006 couple tonetwork/communication infrastructure 1002 along with storage unit 1008.Software applications may execute on any computer in data processingenvironment 1000. Client 1010, client 1012, dashboard 1014 are alsocoupled to network/communication infrastructure 1002. Client 1010 may bea remote computer with a display. Client 1012 may be a mobile deviceconfigured with an application to send or receive information, such asto receive a charge condition of the power supply system 100 or to sendinformation about a calendar of the user. Dashboard 1014 may be locatedinside the electric vehicle and may be configured to send or receive anyof the information discussed herein. A data processing system, such asserver 1004 or server 1006, or clients (client 1010, client 1012,dashboard 1014) may contain data and may have software applications orsoftware tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 10 depicts certain components that are usable in anexample implementation of an embodiment. For example, servers andclients are only examples and do not imply a limitation to aclient-server architecture. As another example, an embodiment can bedistributed across several data processing systems and a data network,as shown. In contrast, another embodiment can be implemented on a singledata processing system within the scope of the illustrative embodiments.Data processing systems (server 1004, server 1006, client 1010, client1012, dashboard 1014) also represent example nodes in a cluster,partitions, and other configurations suitable for implementing anembodiment.

Power supply system 100 includes a traction battery 102 containing oneor more traction and a hybrid range extender battery 124 containing oneor more high energy density hybrid modules 112. As discussed, the one ormore high energy density hybrid modules 112 are configured withchemistry that prioritizes high energy density over available cycle lifeand said each high energy density hybrid module 112 including acorresponding hybrid module controller 118 and a plurality of cellsconnected in series, with each cell of the plurality of cells beingconfigured to be independently measurable by said corresponding hybridmodule controller 118.

Client application 1020, dashboard application 1022, or any otherapplication such as server application 1016 implements an embodimentdescribed herein. Any of the applications can use data from power supplysystem 100 and profile sources to predict power or energy requirements.The applications can also obtain data from storage unit 1008 forpredictive analytics. The applications can also execute in any dataprocessing systems (server 1004 or server 1006, client 1010, client1012, dashboard 1014).

Server 1004, server 1006, storage unit 1008, client 1010, client 1012,dashboard 1014 may couple to network/communication infrastructure 1002using wired connections, wireless communication protocols, or othersuitable data connectivity. Client 1010, client 1012, and dashboard 1014may be, for example, mobile phones, personal computers, or networkcomputers.

In the depicted example, server 1004 may provide data, such as bootfiles, operating system images, and applications to client 1010, client1012, and dashboard 1014. Client 1010, client 1012, and dashboard 1014may be clients to server 1004 in this example. Client 1010, client 1012,and dashboard 1014 or some combination thereof may include their owndata, boot files, operating system images, and applications. Dataprocessing environment 1000 may include additional servers, clients, andother devices that are not shown.

Server 1006 may include a search engine configured to searchinformation, such as terrain condition, speed limits, user feedback,alternate profile sources, GPS information, traffic status, or otherwisedriving characteristics as well as battery measurements (e.g., real-timebattery measurements from individual cells of the high energy densityhybrid modules 112) in response to a request from an operator for powerdelivery as described herein with respect to various embodiments.

In the depicted example, data processing environment 1000 may be theInternet. Network/communication infrastructure 1002 may represent acollection of networks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) and other protocols to communicatewith one another. At the heart of the Internet is a backbone of datacommunication links between major nodes or host computers, includingthousands of commercial, governmental, educational, and other computersystems that route data and messages. Of course, data processingenvironment 1000 also may be implemented as a number of different typesof networks, such as, for example, an intranet, a local area network(LAN), or a wide area network (WAN). FIG. 10 is intended as an exampleand not as an architectural limitation for the different illustrativeembodiments.

Among other uses, data processing environment 1000 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system. Dataprocessing environment 1000 may also employ a service-orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.Data processing environment 1000 may also take the form of a cloud andemploy a cloud computing model of service delivery for enablingconvenient, on-demand network access to a shared pool of configurablecomputing resources (e.g., networks, network bandwidth, servers,processing, memory, storage, applications, virtual machines, andservices) that can be rapidly provisioned and released with minimalmanagement effort or interaction with a provider of the service.

FIG. 11 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented. The data processing system1100 is an example of a computer, such as a client 1010, client 1012,dashboard 1014 or s server 1004, server 1006, in FIG. 10, or anothertype of device in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

The data processing system 1100 is described as a computer only as anexample, without being limited thereto. Implementations in the form ofother devices, in FIG. 10, may modify data processing system 1100, suchas by adding a touch interface and even eliminate certain depictedcomponents from the data processing system 1100 without departing fromthe general description of the operations and functions of the dataprocessing system 1100 described herein.

In the depicted example, the data processing system 1100 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)1102 and South Bridge and input/output (I/O) controller hub (SB/ICH)1104. The processing unit 1106, main memory 1108, and graphics processor1110 are coupled to North Bridge and memory controller hub (NB/MCH)1102. Processing unit 1106 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems. Theprocessing unit 1106 may be a multi-core processor. Graphics processor1110 may be coupled to North Bridge and memory controller hub (NB/MCH)1102 through an accelerated graphics port (AGP) in certainimplementations.

In the depicted example, local area network (LAN) adapter 1112 iscoupled to South Bridge and input/output (I/O) controller hub (SB/ICH)1104. Audio adapter 1116, keyboard and mouse adapter 1120, modem 1122,read-only memory (ROM) 1124, universal serial bus (USB) and other ports1132, and PCI/PCIe devices 1134 are coupled to South Bridge andinput/output (I/O) controller hub (SB/ICH) 1104 through bus 1118. Harddisk drive (HDD) or solid-state drive (SSD) 1126 a and CD-ROM 1130 arecoupled to South Bridge and input/output (I/O) controller hub (SB/ICH)1104 through bus 1128. PCI/PCIe devices 1134 may include, for example,Ethernet adapters, add-in cards, and PC cards for notebook computers.PCI uses a card bus controller, while PCIe does not. Read-only memory(ROM) 1124 may be, for example, a flash binary input/output system(BIOS). Hard disk drive (HDD) or solid-state drive (SSD) 1126 a andCD-ROM 1130 may use, for example, an integrated drive electronics (IDE),serial advanced technology attachment (SATA) interface, or variants suchas external-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO)device 1136 may be coupled to South Bridge and input/output (I/O)controller hub (SB/ICH) 1104 through bus 1118.

Memories, such as main memory 1108, read-only memory (ROM) 1124, orflash memory (not shown), are some examples of computer usable storagedevices. Hard disk drive (HDD) or solid-state drive (SSD) 1126 a, CD-ROM1130, and other similarly usable devices are some examples of computerusable storage devices, including a computer-usable storage medium.

An operating system runs on processing unit 1106. The operating systemcoordinates and provides control of various components within dataprocessing system 1100 in FIG. 11. The operating system may be acommercially available operating system for any type of computingplatform, including but not limited to server systems, personalcomputers, and mobile devices. An object-oriented or another type ofprogramming system may operate in conjunction with the operating systemand provide calls to the operating system from programs or applicationsexecuting on a data processing system 1100.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as application 1016 andclient application 1020 in FIG. 10, are located on storage devices, suchas in the form of codes 1126 b on Hard disk drive (HDD) or solid-statedrive (SSD) 1126 a, and may be loaded into at least one of one or morememories, such as main memory 1108, for execution by processing unit1106. The processes of the illustrative embodiments may be performed byprocessing unit 1106 using computer implemented instructions, which maybe located in a memory, such as, for example, main memory 1108,read-only memory (ROM) 1124, or in one or more peripheral devices.

Furthermore, in one case, code 1126 b may be downloaded over network1114 a from remote system 1114 b, where similar code 1114 c is stored ona storage device 1114 d in another case, code 1126 b may be downloadedover network 1114 a to remote system 1114 b, where downloaded code 1114c is stored on a storage device 1114 d.

The hardware in FIG. 10 and FIG. 11 may vary depending on theimplementation. Other internal hardware or peripheral devices, such asflash memory, equivalent non-volatile memory, or optical disk drives,and the like, may be used in addition to or in place of the hardwaredepicted in FIG. 10 and FIG. 11. In addition, the processes of theillustrative embodiments may be applied to a multiprocessor dataprocessing system.

In some illustrative examples, data processing system 1100 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 1108 or a cache, such as a cache found in NorthBridge and a memory controller hub (NB/MCH) 1102. A processing unit mayinclude one or more processors or CPUs.

The depicted examples in FIG. 10 and FIG. 11 and the above-describedexamples are not meant to imply architectural limitations. For example,the data processing system 1100 also may be a tablet computer, laptopcomputer, or telephone device in addition to taking the form of a mobileor wearable device.

Where a computer or data processing system is described as a virtualmachine, a virtual device, or a virtual component, the virtual machine,virtual device, or the virtual component operates in the manner of thedata processing system 1100 using a virtualized manifestation of some orall components depicted in data processing system 1100. For example, ina virtual machine, virtual device, or virtual component, processing unit1106 is manifested as a virtualized instance of all or some number ofhardware processing units 1106 available in a host data processingsystem, main memory 1108 is manifested as a virtualized instance of allor some portion of main memory 1108 that may be available in the hostdata processing system, and Hard disk drive (HDD) or solid-state drive(SSD) 1126 a is manifested as a virtualized instance of all or someportion of Hard disk drive (HDD) or solid-state drive (SSD) 1126 a thatmay be available in the host data processing system. The host dataprocessing system in such cases is represented by data processing system1100.

Concerning FIG. 12, this figure depicts a diagram of an exampleconfiguration for intelligent power control in accordance with anillustrative embodiment. The intelligent power control can beimplemented using application 1204 in FIG. 12. Application 1204 is anexample of server application 1016, client application 1020, ordashboard application 1022 in FIG. 10. The application 1204 receives ormonitors, for example, in real-time, a set of input data 1202. The inputdata comprises subject electric vehicle parameters 1220 such as acurrent of a high energy density hybrid module, temperature ofindividual cells 114 and that of their neighbors, voltages of the cells114, impedances of the cells 114, state of health of the cells 114, thecapacity of the cells 114, computed polarization curves orcharge-discharge curves 500 of the cells 114 identifying graphitizationplateaus, vehicle maximum speed/acceleration, total vehicle mass,vehicle aerodynamic drag force, location, nearest charging stations,etc. The input data also comprises driving characteristics from profilesources 1226 (user profile 1222, community profile 1224, environmentprofile 1230) such as user preferences, number of planned stops in atrip, average daily driving distance, past driving energy consumptionper mile, duration of stops, calendar data, and environmental data suchas terrain data, road slope angle, air drag coefficient, road rollingresistance coefficient and the like.

In one or more embodiments described herein, characteristics,properties, and/or preferences associated with a user, a community, anenvironment, a subject electric vehicle, a power supply system, etc.,are referred to as “features.” In one or more embodiments, configuration1200 defines and configures an algorithm and/or rule to drive featureselection results. In particular embodiments, an algorithm may include,for example, determining the lowest common value for a feature anddetermining whether the value satisfies the best match within athreshold value (e.g., 90%) of the feature among the users. In anembodiment, the system may prioritize certain features so that featuressuch as safety of battery modules, or time of arrival, or SOH or drivingdistance carry different weights. In an embodiment, after the commondenominator in a fleet of vehicles is found, configuration 1200understands the problems with individual vehicles and extracts andderives the best feature values that will help control a subjectelectric vehicle's power.

In an embodiment, feature extraction component 1214 is configured togenerate relevant features, based on contents of a request fromapplication 1204, for the subject electric vehicle using data from allthe different available features (e.g., subject electric vehicleparameters 1220, the user profile 1222, community profile 1224,environment profile 1230). In the embodiment, feature extractioncomponent 1214 receives a request from an application, 1204 whichincludes at least an identification of a subject electric vehicle 1232and/or a user or location thereof as well as instructions to propose apower output to obtain from one or more high energy density hybridmodules 112 to complete a 10-mile trip. Using the subject electricvehicle 1232 and/or user information, feature extraction component 1214obtains a combination of specific subject electric vehicle parameters1220, user profile information from user profile 1222, community profileinformation from community profile 1224 environmental data fromenvironment profile 1230. In the embodiment, feature extractioncomponent 1214 uses a defined algorithm of prioritization to generatethe features as feature profiles. In a particular embodiment, thefeature profile includes each feature (e.g., 1. current in cells 114, 2.temperature of cells 114, 3. voltages of cells 114, 4. impedances ofcells 114, 5. user calendar, 6. GPS location, 7. destination, 8. rangerequirements, 9. state of health audit report indicative of safety,capacity, and remaining life cycles of the cells 114 and 10. weights aregiven to each feature). Using the extracted features and a trained M/Lmodel 1206 trained using a large number of different datasets, powercontrol module 1216 determines a power output proposal 1212 for thesubject electric vehicle 1232. The main benefit of a hybrid architecturethat employs a chemical composition that prioritizes high energy densityover the available number cycles for which the cells 114 may be chargedand/or discharged is that a range of the traction battery 102 of a powersupply system 100 is significantly increased. Further, by individuallycontrolling the current input and output of the high energy densityhybrid modules 112 having series-connected cells 114, a highly modulararchitecture is obtained that increases safety of the individual cells114 or modules through, for example, an ability to control, upon thedetection of a short circuit, which modules are activated or deactivatedto prevent a localized fault from causing further damage. By modularlycontrolling the high energy density hybrid modules 112 based onmeasurements obtained about their component cells 114, a maximum lifecycle of each high energy density hybrid module 112 can be ensured bythe mere prevention of rapid degradation of cells typically associatedwith undetected cellular issues in parallel connected cells ofconventional solutions. For example, if one cell overheats and isundetected, it may start a chain reaction that affects other cells. Anability to control the current of the individual series-connected cellsand their rate of charging/discharging through balance devices 128 in amodular fashion ensures that the maximum capacities of the cells 114 andthus their available life cycles are preserved. Thus, by employing apower control module 1216 that is based on a machine learning model thattakes preferences and subject electric vehicle health parameters intoaccount, the output of individual high energy density hybrid modules 112can be controlled intelligently and in real-time to efficiently addressthe changing energy demands of the vehicle while allowing the user toachieve said user's range or destination goals without compromising thebenefits afforded by the hybrid architecture. In an embodiment, thepower control module 1216 is trained to maximize said benefits asdiscussed herein.

Turning back to FIG. 12, the feature extraction component 1214 may beincorporated in a deep neural network. The feature extraction component1214 may alternatively be outside the deep neural network. The powercontrol module 1216 uses the obtained features from the featureextraction component 1214 to generate a power output proposal 1212,which may include, for example, information about a power or energy orC-rate 522 required to run one or more bi-directional DC-DC converters502 to meet an immediate or extended distance or range goal based on arequest from application 1204. The power output proposal 1212 may alsocontain information indicative of a predicted state of one or morecomponents of the power supply system 100 and instructions to mitigatepredicted/potential failure modes. Further, the power output proposal1212 may contain information about which one or plurality of high energydensity hybrid modules 112 to obtain the defined power output from, acharging or discharging rate of cells 114 or the traction batterythrough the one or more bi-directional DC-DC converters 502, a time tobegin said charging or discharging, an optimized route, and the like.These examples are not meant to be limiting, and any combination ofthese and other example power output proposals are possible in like ofthe descriptions. The power control module 1216 can be based, forexample, on a neural network such as a recurrent neural network (RNN)and a dynamic neural network (DNN), although it is not meant to belimiting. An RNN is a type of artificial neural network designed torecognize patterns in sequences of data, such as numerical times seriesprediction or forecasting and numerical time series anomaly detectionusing data emanating from sensors, generating image descriptions andtext summarization. RNNs use recurrent connections (going in theopposite direction that the “normal” signal flow), which form cycles inthe network's topology. Computations derived from earlier input are fedback into the network, which gives an RNN a “short-term memory.”Feedback networks, such as RNNs, are dynamic; their ‘state’ is changingcontinuously until they reach an equilibrium point. For this reason,RNNs are particularly suited for detecting relationships across time ina given set of data. Recurrent networks take as their input not just thecurrent input example they see but also what they have perceivedpreviously in time. The decision a recurrent net reached at time stept−1 affects the decision it will reach one moment later at time step t.Thus, recurrent networks have two sources of input, the present and therecent past, which combine to determine how they respond to new data. ADNN relies on a on dynamic declaration of network structure. Inconventional static models, a computation graph (that is a symbolicrepresentation of a computation by a neural network is usually defined)and then examples are fed into an engine that executes this computationand computes its derivatives. However, with a static graph, input sizeshave to be defined at the beginning, which can be non-convenient forapplications with changing inputs. In a DNN however, a dynamicdeclaration strategy is used, wherein a computation graph is implicitlyconstructed by executing procedural code that computes the networkoutputs, with the ability to use different network structures for eachinput. Thus, in a training process, the computation graph can be definedanew for every training example. Thus, the computational graph is builtup dynamically, immediately after input variables are declared. Thegraphs are therefore flexible and allow the modification and inspectionof the internals of the graph at any time. Thus instead of having tomaintain the relationships between all inputs to the neural network andthe layers of the neural network, a decision can be made that upon adefined parameter crossing a threshold level in which its priorityincreases, the structure of the neural network is dynamically changed tocause a corresponding change in the output that addresses the newfunctional requirements of the power supply system 100 caused by thepriority increase, and vice versa. Thus, in dynamic neural networks, theoutputs depend on the current and past values of inputs, outputs, andthe network structure. Neural networks with such feedback areappropriate for system modeling, identification, control and filteringoperations and are particularly important for non-linear dynamical powersupply systems. Of course, the examples are non-limiting and otherexamples can be obtained in light of the specification.

In an illustrative embodiment, the power output proposals 1212 may bepresented by a presentation component 1208 of application 1204. Anadaptation component 1210 is configured to receive input from a user toadapt the power output proposals 1212 if necessary. For example,changing a route proposed by the power control module 1216 causes arecalculation of a proposed power output that takes the terrain anddistance of the new route into consideration.

Feedback component 1218 optionally collects user feedback 1224 relativeto the power output proposals 1212. In one embodiment, application 1204is configured not only to compute power output proposals 1212 but alsoto provide a method for a user to input feedback, where the feedback isindicative of the accuracy of the computed power output proposals 1212.Feedback component 1218 applies the feedback in a machine learningtechnique such as to profiles 1222, 1224, 1230, or to M/L model 1206 inorder to modify the M/L model 1206 for better proposals. In anillustrative embodiment, the application analyzes said feedback input,and the application reinforces the M/L model 1206 of the power controlmodule 1216. If the feedback is positive or unsatisfactory as to theaccuracy of the proposal, the application strengthens or weakensparameters of the M/L model 1206, respectively. In an example, aproposal was to turn on the hybrid range extender battery 124 30 milesbefore reaching a mountain such that and at the top of the mountain,there would be enough battery capacity and power to not need to limitpower at the top of the mountain. However, upon determining that powerat the top of the mountain was actually limited and thus a lower speedthan expected could be maintained, feedback is provided to the powercontrol module 1216 about the inaccuracy of the proposal/prediction.

The input layer of the neural network model can be, for example, avector representative of a current, voltage, or impedance values ofcells 114, pixels of 2D images of terrain data, contextual calendar dataprovided by an NLP engine 1228, etc. In an example, a CNN (convolutionalneural network) uses convolution to extract features from an inputimage. In an embodiment, upon receiving a request to provide a proposal,application 1204 creates an array of values that are input to the inputneurons of the M/L model 1206 to produce an array that contains thepower output proposals 1212.

The neural network M/L model 1206 is trained using various types oftraining data sets, including stored profiles and a large number ofsample vehicular and cell measurements. As shown in FIG. 13, whichdepicts a block diagram of an example training architecture 1302 formachine-learning-based recommendation generation in accordance with anillustrative embodiment, program code extracts various features 1306from training data 1304. The components of the training data 1304 havelabels L. The features are utilized to develop a predictor function,H(x), or a hypothesis, which the program code utilizes as an M/L model1308. In identifying various features in the training data 1304, theprogram code may utilize various techniques including, but not limitedto, mutual information, which is an example of a method that can beutilized to identify features in an embodiment. Other embodiments mayutilize varying techniques to select features, including but not limitedto principal component analysis, diffusion mapping, a Random Forest,and/or recursive feature elimination (a brute force approach toselecting features), to select the features. “P” is the output (e.g.,power output value, high energy density hybrid module 112 from which toobtain power output value, etc.) that can be obtained, which whenreceived, could further trigger the power supply system 100 or vehicleto perform other steps such steps of a stored instruction. The programcode may utilize a machine learning m/l algorithm 1312 to train M/Lmodel 1308, including providing weights for the outputs, so that theprogram code can prioritize various changes based on the predictorfunctions that comprise the M/L model 1308. The output can be evaluatedby a quality metric 1310.

By selecting a diverse set of training data 1304, the program codetrains M/L model 1308 to identify and weight various features of thesubject electric vehicle 1232, drivers, a fleet of vehicles,environmental conditions, etc. To utilize the M/L model 1308, theprogram code obtains (or derives) input data or features to generate anarray of values to input into input neurons of a neural network.Responsive to these inputs, the output neurons of the neural networkproduce an array that includes the power output proposals 1212 to bepresented or used contemporaneously.

With reference to FIG. 14, this figure depicts a flowchart of an exampleprocess 1400 for providing a power output proposal for an electricvehicle in accordance with an illustrative embodiment. Process 1400 canbe implemented using application 1204 in FIG. 12.

In step 1402, process 1400 independently measures, by at least onehybrid module controller (HMC), parameters of each of a plurality ofcells of at least one high energy density hybrid module of a powersupply system. The plurality of cells is connected in series in at leastone high-energy-density hybrid module.

In step 1404, process 1400 receives the cells' measured parameters as atleast a part of a set of subject electric vehicle parameters, indicativeof one or more characteristics of a subject electric vehicle, for use bya power control module. The parameters may include at least current,temperature, and voltage. Other parameters, including a capacity,polarization curve with graphitization plateaus, and impedances (DC IR,AC IR), may be derived from single or time-series measurements of thecurrent, temperature, and voltage. For example, the polarization curvewith graphitization plateaus (where iron interpolation occurs) may beused by process 1400 to interpret the kind of failure happening in acell 114, e.g., loss of lithium, or loss of active sites to storelithium, etc.

In step 1406, process 1400 generates input data using at least the setof subject electric vehicle parameters. In step 1408, process 1400extracts one or more features from the input data, the one or morefeatures are representative of a request for completing a power outputproposal operation such as a calendar of a user who has an upcomingmeeting. The feature extraction may be separate from the model orincluded in one or more layers of the model tuned during training. Theone or more features may also represent attributes obtained from anattributes prioritization 1502 step, as shown in FIG. 15. In theattributes prioritization 1502, one or more attributes 1510 to considerin a power output proposal operation are obtained. The one or moreattributes may have different assigned priorities or weights or may havethe same or even unassigned priority or weight. By training the M/Lmodel 1206 with a large set of different datasets that consider theattributes 1510, different scenarios can be handled by the power controlmodule 1216. In an illustrative and non-limiting embodiment, theattributes 1510 include instructions to maximize or enforce a safetyattribute 1504, maximize a life attribute 1506, and maximize a capacityattribute 1508. In step 1410, process 1400 proposes, using the powercontrol module, at least one power output proposal for the subjectelectric vehicle.

The at least one power output proposal may account for an attribute, dueto an attributes prioritization 1502, a maximum safety 1504 of the powersupply system 100. In an illustrative embodiment, maximizing safetyrepresents accounting for a possible or observed activity happening incell chemistry (e.g., a short circuit between an anode and cathode,manifesting as a self-discharge), wherein the power control module 1216proposes and implements a suspension of an operation of a segment/highenergy density hybrid module 112 of a battery pack without affectingother modules/high energy density hybrid modules 112, a step that isotherwise unavailable in conventional battery packs. The implementationcan also include moving energy away from said high energy density hybridmodule 112 or discharging it and turning it off to isolate it for safetybenefit. Further, by observing an abnormal temperature rise without anycorresponding current changes, power control module, 1216 may deduce afire event, a circuit board failure, or the like and thus discharge acorresponding module proximal to that temp rise to avoid propagation ofthe fire or failure. In another example, by observing a loss inisolation between a chassis 304 and a high-voltage bus, the powercontrol module reduces the state of charge of one or more modules andprovides a service warning, thus maximizing the safety of the powersupply system 100 and thus of the electric vehicle.

The at least one power output proposal may account for an attributeprioritization 1502, a maximum life 1506 of the power supply system 100.In an illustrative embodiment, maximizing life comprises maximizing thehealth of cells 114, i.e., a cell's capability to discharge current. Byobserving an increase in a battery's impedance, the power supply system100 causes a change in the maximum current of the cells 114 to avoidoverheating or “over-stressing” the cells 114 to maximize the life ofthe cells 114. Thus, defined discharge power is determined to complementthe state of health of the cells 114. In the embodiment, impedance ismeasured based on a discharge and recharge of cells and a comparison ofthe discharge parameters and recharge to an ideal standard, wherein thecells 114 are graded in a SOH grading operation. The cells 114 aregraded, for example, as A, B, C, D, and E, with A representing a highSOH and E representing a low SOH. Thus, in the embodiment, all moduleswith cells 114 that are graded D and E may be operated by the powercontrol module 1216 at a C-rate 522 of C/10, and modules having cellsthat are graded B and C may be operated at a C-rate 522 of C/5 andmodules with cells that are graded A may be operated at a C-rate 522 ofC/3, the operated C-rates 522 being a discharge power limit of therespective high energy density hybrid modules 112. The power controlmodule 1216 keeps learning and adjusting according to these limits inconjunction with the safety and capacity attributes. Thus, if one cell114 graded A and its module are taken offline because of a safety issue,another cell 114 may be upgraded from B to A or its module configuredfor harder duty cycles due to the absence of the offlined cell 114).

The at least one power output proposal may account for, due to anattributes prioritization 1502, a maximum capacity 1508 of the powersupply system 100. In an illustrative embodiment, maximizing capacityrecognizes an impedance problem of a cell. For a cell having a highimpedance, the power control module 1216 may operate the correspondinghigh energy density hybrid module 112 at the lowest C-rate 522,providing energy over the longest time and thus maximizing capacity eventhough operating it first would not be likely based solely on lifetimeattribute 1510. Further, for a series string of cells in a group, thecapacity of the group is limited by the weakest cell. If all cells have100 AH and weakest has 60 AH, the weakest cell limits the other cellssince once a charge of zero is reached, the rest of the cells in theseries string cannot be discharged further to avoid damaging the weakestcell. The power control module 1216 operates to avoid divergence ofcapacities between cells in order to protect the weakest cell and notaggravate it. Moreover, the power control module 1216 may implementdischarging and slowly charging the weakest cell in a formation chargeto restore the capacity of the cell.

Thus, in an illustrative embodiment, the power control module 1216operates based on a system of merits and demerits that functions tomaximize life, safety, capacity, and other attributes while alsoconsidering input data such as geography, maximum current and speed andpredicting how to benefit attributes goals by looking at all the inputs.Doing a SOH check frequently/periodically allows the grading ofcells/modules to keep track of their health for decision making. Forexample, using a calendar to see an upcoming trip and SOH check may beconducted to identify a weak battery module to determine if it can beimproved. An identified weak battery module may be charged very slowlyahead of the trip to fix a health problem for use during the trip.

Thus, a computer-implemented method, system or apparatus, and computerprogram product are provided in the illustrative for electric vehiclepower supply and other related features, functions, or operations. Wherean embodiment of a portion thereof is described with respect to a typeof device, the computer-implemented method, system or apparatus, thecomputer program product, or a portion thereof, are adapted orconfigured for use with a suitable and comparable manifestation of thattype of device.

Where an embodiment is described as implemented in an application, thedelivery of the application in a Software as a Service (SaaS) model iscontemplated within the scope of the illustrative embodiments. In a SaaSmodel, the capability of the application implementing an embodiment isprovided to a user by executing the application in a cloudinfrastructure. The user can access the application using a variety ofclient devices through a thin client interface such as a web browser(e.g., web-based e-mail) or other light-weight client-applications. Theuser does not manage or control the underlying cloud infrastructure,including the network, servers, operating systems, or the storage of thecloud infrastructure. In some cases, the user may not even manage orcontrol the capabilities of the SaaS application. In some other cases,the SaaS implementation of the application may permit a possibleexception of limited user-specific application configuration settings.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include the computer-readable storagemedium (or media) having the computer readable program instructionsthereon for causing a processor to carry out aspects of the presentinvention.

The computer-readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer-readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer-readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer-readable storage medium, including but not limited tocomputer-readable storage devices as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide, or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer-readable program instructions described herein can bedownloaded to respective computing/processing devices from acomputer-readable storage medium or to an external computer or externalstorage device via a network, for example, the Internet, a local areanetwork, a wide area network, and/or a wireless network. The network maycomprise copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computers,and/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer-readable programinstructions for storage in a computer-readable storage medium withinthe respective computing/processing device.

The computer-readable program instructions for carrying out operationsof the present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine-dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object-oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer-readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer, and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer-readable program instructions by utilizing state information ofthe computer-readable program instructions to personalize the electroniccircuitry, to perform aspects of the present invention.

Aspects of the present invention are described herein concerningflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that computer readable programinstructions can implement each block of the flowchart illustrationsand/or block diagrams and combinations of blocks in the flowchartillustrations and/or block diagrams.

These computer-readable program instructions may be provided to aprocessor of a general-purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer-readable program instructionsmay also be stored in a computer-readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that thecomputer-readable storage medium having instructions stored thereincomprises an article of manufacture including instructions whichimplement aspects of the function/act specified in the flowchart and/orblock diagram block or blocks.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other devicesto cause a series of operational steps to be performed on the computer,other programmable apparatus or other devices to produce acomputer-implemented process, such that the instructions which executeon the computer, other programmable apparatus, or other device implementthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

1-25. (canceled)
 26. A computer-implemented method comprising the stepsof: independently measuring, by at least one hybrid module controller(HMC), parameters of each cell of a plurality of cells of at least onecorresponding high energy density hybrid module of a power supplysystem, the plurality of cells being connected in series in the at leastone corresponding high energy density hybrid module; receiving themeasured parameters as at least a part of a set of subject electricvehicle parameters, indicative of one or more characteristics of asubject electric vehicle, for use by a power control module; generatinginput data using at least the set of subject electric vehicleparameters; extracting one or more features from the input data, the oneor more features representative of a characteristic of the request forcompleting a power output proposal operation, and proposing, using thepower control module, at least one power output proposal for the subjectelectric vehicle; wherein the power control module operates as a machinelearning engine.
 27. The method of claim 26, further comprising:generating, by attributes prioritization, a set of attributes of thepower supply system to enforce, and proposing the at least one poweroutput proposal based on the attributes.
 28. The method of claim 27,wherein the attributes include a safety attribute of the power supplysystem, a capacity attribute of the power supply system or a life cycleattribute of the power supply system.
 29. The method of claim 26,wherein the power output proposal comprises instructions for the atleast one HMC to manage a power generating mode of the power supplysystem of using the at least one corresponding high energy densityhybrid module by controlling a charging and discharging of the at leastone corresponding high energy density hybrid module by a defined rate.30. The method of claim 26, wherein the at least one corresponding highenergy density hybrid module has a chemistry that prioritizes highenergy density over available cycle life and each cell of the pluralityof cells is independently measurable by said corresponding HMC.
 31. Themethod of claim 26, wherein the input data further comprises informationselected from the group consisting of information about a user of theelectric vehicle, information about a fleet other power supply systemsand information about an environment of the subject electric vehicle.32. The method of claim 26, wherein the input data further comprisescalendar data.
 33. The method of claim 26, further comprising: providingfeedback for the power control module indicative of an accuracy ofproposals in order to reinforce power control module.
 34. The method ofclaim 26, further comprising: charging a traction battery of the powersupply system based on the at least one power output proposal.
 35. Themethod of claim 34, wherein the power output proposal comprisesinstructions for the at least one HMC to manage a power generating modeof the power supply system of using the at least one corresponding highenergy density hybrid module by controlling a charging and dischargingof the at least one corresponding high energy density hybrid module by adefined rate through a bi-directional DC-DC converter.
 36. A computersystem comprising a processor configured to perform the steps including:independently measuring, by at least one hybrid module controller (HMC),parameters of each cell of a plurality of cells of at least onecorresponding high energy density hybrid module of a power supplysystem, the plurality of cells being connected in series in the at leastone corresponding high energy density hybrid module; receiving themeasured parameters as at least a part of a set of subject electricvehicle parameters, indicative of one or more characteristics of asubject electric vehicle, for use by a power control module; generatinginput data using at least the set of subject electric vehicleparameters; extracting one or more features from the input data, the oneor more features representative of a characteristic of the request forcompleting a power output proposal operation, and proposing, using thepower control module, at least one power output proposal for the subjectelectric vehicle; wherein the power control module operates as a machinelearning engine.
 37. The computer system of claim 36, wherein theprocessor is further configured to generate, by attributesprioritization, a set of attributes of the of the power supply system toenforce, and proposing the at least one power output proposal based onthe attributes.
 38. The computer system of claim 36, wherein theattributes include a safety attribute of the power supply system, acapacity attribute of the power supply system or a life cycle attributeof the power supply system.
 39. The computer system of claim 36, whereinthe at least one corresponding high energy density hybrid module has achemistry that prioritizes high energy density over available cycle lifeand each cell of the plurality of cells is independently measurable bysaid corresponding HMC.
 40. The computer system of claim 36, wherein theinput data further comprises calendar data.
 41. A non-transitorycomputer-readable storage medium storing a program which, when executedby a computer system, causes the computer system to perform a procedurecomprising: independently measuring, by at least one hybrid modulecontroller (HMC), parameters of each cell of a plurality of cells of atleast one corresponding high energy density hybrid module of a powersupply system, the plurality of cells being connected in series in theat least one corresponding high energy density hybrid module; receivingthe measured parameters as at least a part of a set of subject electricvehicle parameters, indicative of one or more characteristics of asubject electric vehicle, for use by a power control module; generatinginput data using at least the set of subject electric vehicleparameters; extracting one or more features from the input data, the oneor more features representative of a characteristic of the request forcompleting a power output proposal operation, and proposing, using thepower control module, at least one power output proposal for the subjectelectric vehicle; wherein the power control module operates as a machinelearning engine.
 42. The non-transitory computer-readable storage mediumof claim 41, wherein the computer system generates, by attributesprioritization, a set of attributes of the of the power supply system toenforce, and proposing the at least one power output proposal based onthe attributes.
 43. The non-transitory computer-readable storage mediumof claim 41, wherein the attributes include a safety attribute of thepower supply system, a capacity attribute of the power supply system ora life cycle attribute of the power supply system.
 44. Thenon-transitory computer-readable storage medium of claim 41, wherein theat least one corresponding high energy density hybrid module has achemistry that prioritizes high energy density over available cycle lifeand each cell of the plurality of cells is independently measurable bysaid corresponding HMC.
 45. The non-transitory computer-readable storagemedium of claim 41, wherein the input data further comprises calendardata. 46-67. (canceled)